德纳姆
代谢物
生物
代谢组学
老化
DNA甲基化
内科学
内分泌学
生理学
生物信息学
医学
遗传学
基因表达
基因
作者
Kexin Xu,Belinda Hernández,Thalida Em Arpawong,Stéphane Camuzeaux,Elena Chekmeneva,Eileen M. Crimmins,Paul Elliott,Giovanni Fiorito,Beatriz Jiménez,Rose Anne Kenny,Cathal McCrory,Sinéad McLoughlin,Rui Pinto,Caroline Sands,P. Vineis,Chung‐Ho E. Lau,Oliver Robinson
摘要
ABSTRACT Metabolomics and epigenomics have been used to develop ‘ageing clocks’ that assess biological age and identify ‘accelerated ageing’. While metabolites are subject to short‐term variation, DNA methylation (DNAm) may capture longer‐term metabolic changes. We aimed to develop a hybrid DNAm‐metabolic clock using DNAm as metabolite surrogates (‘DNAm‐metabolites’) for age prediction. Within the UK Airwave cohort ( n = 820), we developed DNAm metabolites by regressing 594 metabolites on DNAm and selected 177 DNAm metabolites and 193 metabolites to construct ‘DNAm‐metabolic’ and ‘metabolic’ clocks. We evaluated clocks in their age prediction and association with noncommunicable disease risk factors. We additionally validated the DNAm‐metabolic clock for the prediction of age and health outcomes in The Irish Longitudinal Study of Ageing (TILDA, n = 488) and the Health and Retirement Study (HRS, n = 4018). Around 70% of DNAm metabolites showed significant metabolite correlations (Pearson's r : > 0.30, p < 10 −4 ) in the Airwave test set and overall stronger age associations than metabolites. The DNAm‐metabolic clock was enriched for metabolic traits and was associated ( p < 0.05) with male sex, heavy drinking, anxiety, depression and trauma. In TILDA and HRS, the DNAm‐metabolic clock predicted age ( r = 0.73 and 0.69), disability and gait speed ( p < 0.05). In HRS, it additionally predicted time to death, diabetes, cardiovascular disease, frailty and grip strength. DNAm metabolite surrogates may facilitate metabolic studies using only DNAm data. Clocks built from DNAm metabolites provided a novel approach to assess metabolic ageing, potentially enabling early detection of metabolic‐related diseases for personalised medicine.
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